1. Locate “odd responses” based on duration and number of NA in each response
  2. Duration: a) Keep responses in (2.5%, 97.5%), b) responses in (5%, 95%)
  3. NAs: a) Responses in (10%, 90%)
san %>% 
  #filter(!is.na(zipcode)) %>% 
  ggplot(aes(x = zipcode, fill = duration_min_out)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2, color = "black") +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  #facet_grid(~ zipcode) +
  ggtitle("Duration between 0.7 min and 97.3 min = NORMAL (95% of responses)") +
  xlab("Zip code") + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, size = 9), 
        panel.background = element_rect(fill = "gray95"))

san %>% 
  #filter(!is.na(zipcode)) %>% 
  ggplot(aes(x = zipcode, fill = duration_min_out2)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2, color = "black") +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  #facet_grid(~ zipcode) +
  ggtitle("Duration between 1.36 min and 71.2 min = NORMAL (90% of reponses)") +
  xlab("Zip code") + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, size = 9), 
        panel.background = element_rect(fill = "gray95"))

san %>% 
  #filter(!is.na(zipcode)) %>% 
  ggplot(aes(x = zipcode, fill = nas_out)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2, color = "black") +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  #facet_grid(~ zipcode) +
  ggtitle("Number of NAs between 6 and 91 = NORMAL") +
  xlab("Zip code") + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, size = 9), 
        panel.background = element_rect(fill = "gray95"))

plot_ly(data = san, x = ~nas_pct, y = ~duration, color = ~duration_min_out2) %>% 
  layout(title = "Duration (min) by NAs by response (%)", 
         yaxis = list(title = "Duration (min)"),
         xaxis = list(title = "Percentage of NA values"))
san %>% 
  filter(!is.na(zipcode)) %>% 
  ggplot() +
  geom_boxplot(aes(y = nas_pct, x = duration_min_out)) +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  ggtitle("NA dist. by duration in minutes") +
  xlab("Duration between 0.7 min and 97.3 min = NORMAL (95% of responses)") + 
  theme_classic() +
  theme(axis.text.x = element_text(size = 9), 
        panel.background = element_rect(fill = "gray95"))

san %>% 
  filter(!is.na(zipcode)) %>% 
  ggplot() +
  geom_boxplot(aes(y = nas_pct, x = duration_min_out, fill = as.factor(zipcode))) +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  ggtitle("NA dist. by duration in minutes by zip code") +
  xlab("Duration between 0.7 min and 97.3 min = NORMAL (95% of responses)") + 
  theme_classic() +
  theme(axis.text.x = element_text(size = 9), 
        panel.background = element_rect(fill = "gray95"))

san %>% 
  filter(!is.na(zipcode), duration < 900) %>% 
  ggplot() +
  geom_boxplot(aes(y = duration, fill = as.factor(zipcode), x = nas_out)) +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  ggtitle("Duration (min) by zip code and NA distribution") +
  xlab("Zip code") + 
  theme_classic() +
  theme(axis.text.x = element_text(size = 9), 
        panel.background = element_rect(fill = "gray95"))

load("~/GitHub/san/sa_survey.RData")

san %>% 
  filter(health != "NA") %>% 
  ggplot(aes(x = race_eth, fill = health)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by the feeling nervous, anxious, or on edge") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(anxious != "NA", worry != "NA", interest != "NA", down!= "NA") %>% 
  ggplot(aes(x = race_eth, fill = anxious)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by the feeling nervous, anxious, or on edge") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(anxious != "NA", worry != "NA", interest != "NA", down!= "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = worry)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by not being able to stop or control worrying") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(anxious != "NA", worry != "NA", interest != "NA", down!= "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = interest)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by having little interest or pleasure in doing things") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(anxious != "NA", worry != "NA", interest != "NA", down!= "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = down)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by feeling down, depressed, or hopeless") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(pre_c19_ph != "NA", pre_c19_mh != "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = pre_c19_ph)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "D", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and physical health prior to COVID-19") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(pre_c19_ph != "NA", pre_c19_mh != "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = pre_c19_mh)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "D", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and mental health prior to COVID-19") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))
san %>% 
  filter(front != "NA") %>% 
  ggplot(aes(x = race_eth, fill = front)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2, color = "white") +
  scale_fill_viridis_d(option = "B", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and identification as frontline worker") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(front != "NA",  gender != "NA", work_hrs > 0 ) %>% 
  ggplot() +
  geom_col(aes(x = work_hrs, y = race_eth, color = gender, fill = gender), position = "dodge", width = .5) +
  facet_grid(~ zip) +
  scale_color_viridis_d(option = "E", begin = .2, end = .8) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  ggtitle("Responses by zip code and working hours per week") +
  xlab("Hours per week") + 
  ylab(NULL) +
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

Part I.

view(dfSummary(san[, 5:173], plain.ascii = F, graph.magnif = .75, labels.col = T, max.string.width = 15), method = "render")

Data Frame Summary

san

Dimensions: 1013 x 169
Duplicates: 0
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 Progress [numeric] Mean (sd) : 91.1 (26.8) min < med < max: 0 < 100 < 100 IQR (CV) : 0 (0.3) 35 distinct values 1013 (100.0%) 0 (0.0%)
2 Duration (in seconds) [numeric] Mean (sd) : 1746.6 (3597.5) min < med < max: 10 < 1219 < 82589 IQR (CV) : 1220 (2.1) 841 distinct values 1013 (100.0%) 0 (0.0%)
3 Finished [numeric] Min : 0 Mean : 0.9 Max : 1
0:110(10.9%)
1:903(89.1%)
 1013 (100.0%) 0 (0.0%)
4 RecordedDate [POSIXct, POSIXt] min : 2021-02-22 14:32:03 med : 2021-02-23 12:42:57 max : 2021-03-04 02:05:00 range : 9d 11H 32M 57S 984 distinct values 1013 (100.0%) 0 (0.0%)
5 ResponseId [character] 1. R_01F3YkgcZF3LT 2. R_0Ak2tVjB09xye 3. R_0Au0Xh2TGsWoJ 4. R_0BXTlGwZGOnMe 5. R_0c60ihjCKT6Rb 6. R_0CFUnzIgwaZ1t 7. R_0fxfBNmeY6qlJ 8. R_0HXvwzj23fxTW 9. R_0ilrJc7CA6LRe 10. R_0IpsG07KZKdcO [ 1003 others ]
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1003(99.0%)
 1013 (100.0%) 0 (0.0%)
6 LocationLatitude [numeric] Mean (sd) : 33.6 (5.2) min < med < max: 21.4 < 32.9 < 61.6 IQR (CV) : 7.8 (0.2) 431 distinct values 903 (89.1%) 110 (10.9%)
7 LocationLongitude [numeric] Mean (sd) : -92.6 (44.6) min < med < max: -158 < -98.5 < 117.3 IQR (CV) : 21.4 (-0.5) 432 distinct values 903 (89.1%) 110 (10.9%)
8 DistributionChannel [character] 1. anonymous
1013(100.0%)
 1013 (100.0%) 0 (0.0%)
9 UserLanguage [character] 1. EN 2. ES
1012(99.9%)
1(0.1%)
 1013 (100.0%) 0 (0.0%)
10 consent [numeric] Min : 1 Mean : 1 Max : 2
1:1012(99.9%)
2:1(0.1%)
 1013 (100.0%) 0 (0.0%)
11 disagree [numeric] 1 distinct value
1:1(100.0%)
 1 (0.1%) 1012 (99.9%)
12 zipcode [numeric] Mean (sd) : 1.5 (0.6) min < med < max: 1 < 1 < 3 IQR (CV) : 1 (0.4)
1:543(55.7%)
2:369(37.8%)
3:63(6.5%)
 975 (96.2%) 38 (3.8%)
13 per_care_1 [numeric] Mean (sd) : 7.9 (1.7) min < med < max: 0.6 < 8 < 20.5 IQR (CV) : 1.1 (0.2) 98 distinct values 896 (88.5%) 117 (11.5%)
14 per_care_2 [numeric] Mean (sd) : 1.9 (7.1) min < med < max: -99 < 1.9 < 17.9 IQR (CV) : 1.4 (3.7) 92 distinct values 896 (88.5%) 117 (11.5%)
15 per_care_3 [numeric] Mean (sd) : -0.5 (15.8) min < med < max: -99 < 1.5 < 21.4 IQR (CV) : 1 (-30.5) 87 distinct values 896 (88.5%) 117 (11.5%)
16 per_care_4 [numeric] Mean (sd) : 0 (13.5) min < med < max: -99 < 1 < 17.8 IQR (CV) : 0.9 (-477.6) 83 distinct values 896 (88.5%) 117 (11.5%)
17 per_care_wknd_1 [numeric] Min : 4 Mean : 4.6 Max : 5
4:394(44.2%)
5:498(55.8%)
 892 (88.1%) 121 (11.9%)
18 per_care_wknd_2 [numeric] Min : 4 Mean : 4.5 Max : 5
4:435(48.9%)
5:454(51.1%)
 889 (87.8%) 124 (12.2%)
19 per_care_wknd_3 [numeric] Min : 4 Mean : 4.5 Max : 5
4:472(54.2%)
5:399(45.8%)
 871 (86.0%) 142 (14.0%)
20 per_care_wknd_4 [numeric] Min : 4 Mean : 4.4 Max : 5
4:522(59.6%)
5:354(40.4%)
 876 (86.5%) 137 (13.5%)
21 pc_wknd_time_1 [numeric] Mean (sd) : 9 (5.3) min < med < max: -99 < 9 < 19 IQR (CV) : 1.9 (0.6) 86 distinct values 498 (49.2%) 515 (50.8%)
22 pc_wknd_time_2 [numeric] Mean (sd) : 3.5 (5.6) min < med < max: -99 < 2.8 < 19.1 IQR (CV) : 2.5 (1.6) 96 distinct values 453 (44.7%) 560 (55.3%)
23 pc_wknd_time_3 [numeric] Mean (sd) : 2.3 (7.6) min < med < max: -99 < 2 < 20.6 IQR (CV) : 1.5 (3.3) 86 distinct values 399 (39.4%) 614 (60.6%)
24 pc_wknd_time_4 [numeric] Mean (sd) : 2.1 (6) min < med < max: -99 < 1.5 < 19.7 IQR (CV) : 1.7 (2.9) 73 distinct values 353 (34.8%) 660 (65.2%)
25 per_care_covid_1 [numeric] Mean (sd) : 3.5 (1) min < med < max: 1 < 3 < 5 IQR (CV) : 1 (0.3)
1:32(3.6%)
2:63(7.1%)
3:364(41.0%)
4:251(28.3%)
5:177(20.0%)
 887 (87.6%) 126 (12.4%)
26 per_care_covid_2 [numeric] Mean (sd) : 3.5 (0.9) min < med < max: 1 < 3 < 5 IQR (CV) : 1 (0.2)
1:2(0.2%)
2:87(9.8%)
3:424(47.8%)
4:254(28.6%)
5:120(13.5%)
 887 (87.6%) 126 (12.4%)
27 per_care_covid_3 [numeric] Mean (sd) : 3.5 (0.9) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:10(1.1%)
2:70(7.9%)
3:445(50.2%)
4:233(26.3%)
5:124(14.0%)
6:5(0.6%)
 887 (87.6%) 126 (12.4%)
28 per_care_covid_4 [numeric] Mean (sd) : 3.3 (0.9) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:17(1.9%)
2:94(10.6%)
3:459(51.7%)
4:225(25.4%)
5:88(9.9%)
6:4(0.5%)
 887 (87.6%) 126 (12.4%)
29 st_act_1 [numeric] Mean (sd) : -19.5 (44.6) min < med < max: -99 < 3.8 < 18.4 IQR (CV) : 6.6 (-2.3) 121 distinct values 886 (87.5%) 127 (12.5%)
30 st_act_2 [numeric] Mean (sd) : -26 (45.9) min < med < max: -99 < 1.2 < 20.7 IQR (CV) : 101.5 (-1.8) 99 distinct values 886 (87.5%) 127 (12.5%)
31 st_act_3 [numeric] Mean (sd) : -30.9 (47.6) min < med < max: -99 < 0.7 < 21.4 IQR (CV) : 100.5 (-1.5) 92 distinct values 886 (87.5%) 127 (12.5%)
32 st_act_wknd_1 [numeric] Min : 3 Mean : 3.5 Max : 4
3:336(49.3%)
4:346(50.7%)
 682 (67.3%) 331 (32.7%)
33 st_act_wknd_2 [numeric] Min : 3 Mean : 3.5 Max : 4
3:297(46.3%)
4:344(53.7%)
 641 (63.3%) 372 (36.7%)
34 st_act_wknd_3 [numeric] Min : 3 Mean : 3.5 Max : 4
3:304(50.8%)
4:294(49.2%)
 598 (59.0%) 415 (41.0%)
35 sa_wknd_time_1 [numeric] Mean (sd) : -0.7 (20.3) min < med < max: -99 < 2.5 < 13.3 IQR (CV) : 2.3 (-27.4) 83 distinct values 347 (34.3%) 666 (65.7%)
36 sa_wknd_time_2 [numeric] Mean (sd) : 2.2 (12.8) min < med < max: -99 < 2.2 < 19 IQR (CV) : 3.7 (5.8) 97 distinct values 344 (34.0%) 669 (66.0%)
37 sa_wknd_time_3 [numeric] Mean (sd) : -13.8 (37.7) min < med < max: -99 < 1 < 16.4 IQR (CV) : 2.8 (-2.7) 73 distinct values 296 (29.2%) 717 (70.8%)
38 st_act_covid_1 [numeric] Mean (sd) : 3.6 (1.5) min < med < max: 1 < 3 < 6 IQR (CV) : 2 (0.4)
1:44(5.0%)
2:158(17.9%)
3:307(34.7%)
4:139(15.7%)
5:83(9.4%)
6:153(17.3%)
 884 (87.3%) 129 (12.7%)
39 st_act_covid_2 [numeric] Mean (sd) : 3.7 (1.4) min < med < max: 1 < 3 < 6 IQR (CV) : 2 (0.4)
1:30(3.4%)
2:144(16.3%)
3:287(32.5%)
4:182(20.6%)
5:79(8.9%)
6:162(18.3%)
 884 (87.3%) 129 (12.7%)
40 st_act_covid_3 [numeric] Mean (sd) : 3.5 (1.7) min < med < max: 1 < 3 < 6 IQR (CV) : 3 (0.5)
1:114(12.9%)
2:129(14.6%)
3:294(33.3%)
4:106(12.0%)
5:41(4.6%)
6:200(22.6%)
 884 (87.3%) 129 (12.7%)
41 own_device [numeric] Mean (sd) : 2.7 (0.7) min < med < max: 1 < 3 < 4 IQR (CV) : 0 (0.2)
1:97(11.0%)
2:51(5.8%)
3:719(81.3%)
4:17(1.9%)
 884 (87.3%) 129 (12.7%)
42 dev_act_1 [numeric] Mean (sd) : -2 (22.1) min < med < max: -99 < 2 < 16.8 IQR (CV) : 2.5 (-11.1) 110 distinct values 861 (85.0%) 152 (15.0%)
43 dev_act_7 [numeric] Mean (sd) : -0.2 (18.3) min < med < max: -99 < 2 < 18.6 IQR (CV) : 2.9 (-80.3) 109 distinct values 861 (85.0%) 152 (15.0%)
44 dev_act_3 [numeric] Mean (sd) : -13.2 (35.9) min < med < max: -99 < 1 < 20.8 IQR (CV) : 1.2 (-2.7) 84 distinct values 861 (85.0%) 152 (15.0%)
45 dev_act_6 [numeric] Mean (sd) : -11.1 (34.6) min < med < max: -99 < 1.3 < 20.9 IQR (CV) : 2.2 (-3.1) 102 distinct values 861 (85.0%) 152 (15.0%)
46 dev_act_wknd_1 [numeric] Min : 2 Mean : 2.4 Max : 3
2:466(56.9%)
3:353(43.1%)
 819 (80.8%) 194 (19.2%)
47 dev_act_wknd_2 [numeric] Min : 2 Mean : 2.4 Max : 3
2:484(58.1%)
3:349(41.9%)
 833 (82.2%) 180 (17.8%)
48 dev_act_wknd_3 [numeric] Min : 2 Mean : 2.5 Max : 3
2:339(46.2%)
3:395(53.8%)
 734 (72.5%) 279 (27.5%)
49 dev_act_wknd_4 [numeric] Min : 2 Mean : 2.4 Max : 3
2:460(61.5%)
3:288(38.5%)
 748 (73.8%) 265 (26.2%)
50 da_wknd_time_1 [numeric] Mean (sd) : 3.3 (5.1) min < med < max: -99 < 3 < 11.6 IQR (CV) : 1.9 (1.6) 83 distinct values 465 (45.9%) 548 (54.1%)
51 da_wknd_time_2 [numeric] Mean (sd) : 3.4 (5.4) min < med < max: -99 < 2.9 < 17.8 IQR (CV) : 2.5 (1.6) 102 distinct values 483 (47.7%) 530 (52.3%)
52 da_wknd_time_3 [numeric] Mean (sd) : 1.5 (11.3) min < med < max: -99 < 1.9 < 20.3 IQR (CV) : 2.1 (7.6) 81 distinct values 339 (33.5%) 674 (66.5%)
53 da_wknd_time_6 [numeric] Mean (sd) : 2.2 (12) min < med < max: -99 < 2.7 < 18 IQR (CV) : 2.4 (5.4) 99 distinct values 459 (45.3%) 554 (54.7%)
54 dev_act_covid_1 [numeric] Mean (sd) : 3.7 (1) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:25(2.9%)
2:65(7.6%)
3:287(33.5%)
4:297(34.7%)
5:168(19.6%)
6:14(1.6%)
 856 (84.5%) 157 (15.5%)
55 dev_act_covid_2 [numeric] Mean (sd) : 3.5 (1) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:13(1.5%)
2:118(13.8%)
3:269(31.4%)
4:311(36.3%)
5:136(15.9%)
6:9(1.1%)
 856 (84.5%) 157 (15.5%)
56 dev_act_covid_3 [numeric] Mean (sd) : 3.5 (1) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:18(2.1%)
2:93(10.9%)
3:362(42.3%)
4:249(29.1%)
5:109(12.7%)
6:25(2.9%)
 856 (84.5%) 157 (15.5%)
57 dev_act_covid_4 [numeric] Mean (sd) : 3.6 (0.9) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:9(1.1%)
2:69(8.1%)
3:348(40.7%)
4:294(34.3%)
5:130(15.2%)
6:6(0.7%)
 856 (84.5%) 157 (15.5%)
58 dev_act_covid_5 [numeric] Mean (sd) : 3.6 (0.9) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.2)
1:12(1.4%)
2:56(6.5%)
3:314(36.7%)
4:323(37.7%)
5:145(16.9%)
6:6(0.7%)
 856 (84.5%) 157 (15.5%)
59 dev_act_covid_6 [numeric] Mean (sd) : 3.5 (0.9) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:9(1.1%)
2:88(10.3%)
3:352(41.1%)
4:274(32.0%)
5:124(14.5%)
6:9(1.1%)
 856 (84.5%) 157 (15.5%)
60 dev_act_covid_7 [numeric] Mean (sd) : 3.8 (1) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:16(1.9%)
2:52(6.1%)
3:252(29.4%)
4:333(38.9%)
5:162(18.9%)
6:41(4.8%)
 856 (84.5%) 157 (15.5%)
61 other_act_1 [numeric] Mean (sd) : -11.7 (35.7) min < med < max: -99 < 1.4 < 18.4 IQR (CV) : 2.4 (-3) 104 distinct values 871 (86.0%) 142 (14.0%)
62 other_act_2 [numeric] Mean (sd) : -2.8 (21.2) min < med < max: -99 < 1.1 < 15 IQR (CV) : 1.3 (-7.5) 81 distinct values 871 (86.0%) 142 (14.0%)
63 other_act_3 [numeric] Mean (sd) : -2.6 (20.4) min < med < max: -99 < 1.1 < 20.9 IQR (CV) : 1.1 (-8) 81 distinct values 871 (86.0%) 142 (14.0%)
64 other_act_4 [numeric] Mean (sd) : -24.1 (44.1) min < med < max: -99 < 0.7 < 16.8 IQR (CV) : 100.5 (-1.8) 84 distinct values 871 (86.0%) 142 (14.0%)
65 other_act_wknd_1 [numeric] Min : 2 Mean : 2.6 Max : 3
2:313(41.8%)
3:435(58.2%)
 748 (73.8%) 265 (26.2%)
66 other_act_wknd_2 [numeric] Min : 2 Mean : 2.4 Max : 3
2:481(57.8%)
3:351(42.2%)
 832 (82.1%) 181 (17.9%)
67 other_act_wknd_3 [numeric] Min : 2 Mean : 2.5 Max : 3
2:398(47.7%)
3:437(52.3%)
 835 (82.4%) 178 (17.6%)
68 other_act_wknd_4 [numeric] Min : 2 Mean : 2.5 Max : 3
2:316(48.5%)
3:335(51.5%)
 651 (64.3%) 362 (35.7%)
69 oa_wknd_time_1 [numeric] Mean (sd) : 2.8 (7.9) min < med < max: -99 < 2.4 < 17.8 IQR (CV) : 2.5 (2.8) 102 distinct values 575 (56.8%) 438 (43.2%)
70 oa_wknd_time_2 [numeric] Mean (sd) : 1.6 (9.4) min < med < max: -99 < 2 < 22.2 IQR (CV) : 1.4 (5.9) 83 distinct values 614 (60.6%) 399 (39.4%)
71 oa_wknd_time_3 [numeric] Mean (sd) : 1.3 (9.4) min < med < max: -99 < 1.5 < 18.8 IQR (CV) : 1.3 (7.2) 82 distinct values 617 (60.9%) 396 (39.1%)
72 oa_wknd_time_4 [numeric] Mean (sd) : 0 (14.4) min < med < max: -99 < 1.2 < 20.6 IQR (CV) : 1.5 (-1026) 79 distinct values 500 (49.4%) 513 (50.6%)
73 other_act_covid_1 [numeric] Mean (sd) : 3.5 (1) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:26(3.0%)
2:72(8.3%)
3:394(45.4%)
4:223(25.7%)
5:128(14.8%)
6:24(2.8%)
 867 (85.6%) 146 (14.4%)
74 other_act_covid_2 [numeric] Mean (sd) : 3.6 (1) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:12(1.4%)
2:82(9.5%)
3:321(37.0%)
4:291(33.6%)
5:152(17.5%)
6:9(1.0%)
 867 (85.6%) 146 (14.4%)
75 other_act_covid_3 [numeric] Mean (sd) : 3.5 (0.9) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:12(1.4%)
2:72(8.3%)
3:379(43.7%)
4:260(30.0%)
5:130(15.0%)
6:14(1.6%)
 867 (85.6%) 146 (14.4%)
76 other_act_covid_4 [numeric] Mean (sd) : 3.5 (1.1) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:27(3.1%)
2:75(8.7%)
3:395(45.6%)
4:236(27.2%)
5:76(8.8%)
6:58(6.7%)
 867 (85.6%) 146 (14.4%)
77 pre_electric [numeric] Mean (sd) : 169.6 (456.9) min < med < max: 1 < 100 < 9000 IQR (CV) : 80 (2.7) 107 distinct values 864 (85.3%) 149 (14.7%)
78 post_electric [numeric] Mean (sd) : 167 (280.3) min < med < max: 2 < 110 < 4000 IQR (CV) : 92.5 (1.7) 106 distinct values 863 (85.2%) 150 (14.8%)
79 pre_gas [numeric] Mean (sd) : 214.2 (471.8) min < med < max: 0 < 80 < 8000 IQR (CV) : 150 (2.2) 100 distinct values 861 (85.0%) 152 (15.0%)
80 post_gas [numeric] Mean (sd) : 172 (497.4) min < med < max: 0 < 80 < 9000 IQR (CV) : 70 (2.9) 98 distinct values 859 (84.8%) 154 (15.2%)
81 num_vehicles [numeric] Mean (sd) : 1.5 (0.9) min < med < max: 0 < 1 < 20 IQR (CV) : 1 (0.6)
0:26(3.0%)
1:471(54.8%)
2:329(38.3%)
3:30(3.5%)
4:1(0.1%)
5:1(0.1%)
20:1(0.1%)
 859 (84.8%) 154 (15.2%)
82 pre_trans [numeric] Mean (sd) : 374.3 (587.3) min < med < max: 0 < 210 < 9000 IQR (CV) : 280 (1.6) 94 distinct values 857 (84.6%) 156 (15.4%)
83 post_trans [numeric] Mean (sd) : 215.9 (611.6) min < med < max: 0 < 100 < 8080 IQR (CV) : 100 (2.8) 92 distinct values 857 (84.6%) 156 (15.4%)
84 health [ordered, factor] 1. Excellent 2. Very good 3. Good 4. Fair 5. Poor
141(16.5%)
339(39.6%)
324(37.9%)
42(4.9%)
10(1.2%)
 856 (84.5%) 157 (15.5%)
85 mental_health_anxious [numeric] Mean (sd) : 0 (14.6) min < med < max: -99 < 2 < 4 IQR (CV) : 2 (-1244.3)
-99:18(2.1%)
1:248(29.0%)
2:308(36.0%)
3:216(25.3%)
4:65(7.6%)
 855 (84.4%) 158 (15.6%)
86 mental_health_worry [numeric] Mean (sd) : -0.9 (16.7) min < med < max: -99 < 2 < 4 IQR (CV) : 2 (-19.6)
-99:24(2.8%)
1:328(38.4%)
2:247(28.9%)
3:201(23.5%)
4:55(6.4%)
 855 (84.4%) 158 (15.6%)
87 mental_health_interest [numeric] Mean (sd) : -0.2 (15.3) min < med < max: -99 < 2 < 4 IQR (CV) : 2 (-66.2)
-99:20(2.3%)
1:230(26.9%)
2:325(38.0%)
3:218(25.5%)
4:62(7.3%)
 855 (84.4%) 158 (15.6%)
88 mental_health_down [numeric] Mean (sd) : -1.1 (17.4) min < med < max: -99 < 2 < 4 IQR (CV) : 2 (-16.4)
-99:26(3.0%)
1:323(37.8%)
2:231(27.0%)
3:216(25.3%)
4:59(6.9%)
 855 (84.4%) 158 (15.6%)
89 physical_health [numeric] Mean (sd) : 3 (3.4) min < med < max: 0 < 2 < 28 IQR (CV) : 5 (1.1) 17 distinct values 854 (84.3%) 159 (15.7%)
90 mental_health [numeric] Mean (sd) : 3.9 (4.8) min < med < max: 0 < 2 < 30 IQR (CV) : 5 (1.2) 23 distinct values 853 (84.2%) 160 (15.8%)
91 pre_phy_health [numeric] Mean (sd) : 1.6 (7.7) min < med < max: -99 < 2 < 3 IQR (CV) : 1 (4.9)
-99:5(0.6%)
1:76(8.9%)
2:554(64.9%)
3:218(25.6%)
 853 (84.2%) 160 (15.8%)
92 pre_mental_health [numeric] Mean (sd) : 1.4 (7.7) min < med < max: -99 < 2 < 3 IQR (CV) : 0 (5.3)
-99:5(0.6%)
1:170(19.9%)
2:473(55.5%)
3:205(24.0%)
 853 (84.2%) 160 (15.8%)
93 days_poor_health [numeric] Mean (sd) : 3.1 (4.4) min < med < max: 0 < 2 < 25 IQR (CV) : 4 (1.4) 22 distinct values 853 (84.2%) 160 (15.8%)
94 impairment [numeric] Mean (sd) : -3.4 (22) min < med < max: -99 < 2 < 2 IQR (CV) : 1 (-6.4)
-99:43(5.0%)
1:285(33.4%)
2:525(61.5%)
 853 (84.2%) 160 (15.8%)
95 impairment_hsh...103 [numeric] Mean (sd) : -5 (24.9) min < med < max: -99 < 2 < 2 IQR (CV) : 1 (-5)
-99:56(6.6%)
1:273(32.0%)
2:524(61.4%)
 853 (84.2%) 160 (15.8%)
96 num_hsh_impair [numeric] Mean (sd) : 0.6 (1) min < med < max: 0 < 0 < 12 IQR (CV) : 1 (1.7)
0:517(60.6%)
1:212(24.9%)
2:100(11.7%)
3:11(1.3%)
4:7(0.8%)
5:1(0.1%)
6:3(0.4%)
10:1(0.1%)
12:1(0.1%)
 853 (84.2%) 160 (15.8%)
97 major_impairment [numeric] Mean (sd) : -7.1 (38) min < med < max: -99 < 7 < 14 IQR (CV) : 11 (-5.3) 15 distinct values 284 (28.0%) 729 (72.0%)
98 other_major_imp [numeric] 1 distinct value
0:1(100.0%)
 1 (0.1%) 1012 (99.9%)
99 impairment_hsh...107 [numeric] Mean (sd) : -8 (38.7) min < med < max: -99 < 7 < 14 IQR (CV) : 11 (-4.9) 15 distinct values 271 (26.8%) 742 (73.2%)
100 other_impairment_hsh [character] 1. A mild cold mak
1(100.0%)
 1 (0.1%) 1012 (99.9%)
101 days_impairment_1 [numeric] Mean (sd) : -30.8 (50.8) min < med < max: -99 < 2 < 31 IQR (CV) : 106 (-1.6) 26 distinct values 851 (84.0%) 162 (16.0%)
102 weeks_impairment_4 [numeric] Mean (sd) : -52.9 (50.2) min < med < max: -99 < -99 < 7 IQR (CV) : 100 (-1)
-99:461(54.2%)
0:63(7.4%)
1:153(18.0%)
2:84(9.9%)
3:48(5.6%)
4:25(2.9%)
5:9(1.1%)
6:4(0.5%)
7:4(0.5%)
 851 (84.0%) 162 (16.0%)
103 months_impairment_1 [numeric] Mean (sd) : -60.1 (49.2) min < med < max: -99 < -99 < 10 IQR (CV) : 100 (-0.8) 12 distinct values 851 (84.0%) 162 (16.0%)
104 years_impairment_1 [numeric] Mean (sd) : -63.1 (48.2) min < med < max: -99 < -99 < 7 IQR (CV) : 99 (-0.8)
-99:547(64.3%)
0:95(11.2%)
1:99(11.6%)
2:58(6.8%)
3:21(2.5%)
4:15(1.8%)
5:9(1.1%)
6:3(0.4%)
7:4(0.5%)
 851 (84.0%) 162 (16.0%)
105 med_device_1 [numeric] Mean (sd) : -51.1 (49.7) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:253(29.7%)
1:157(18.4%)
 851 (84.0%) 162 (16.0%)
106 med_device_2 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:338(39.7%)
1:72(8.5%)
 851 (84.0%) 162 (16.0%)
107 med_device_3 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:350(41.1%)
1:60(7.1%)
 851 (84.0%) 162 (16.0%)
108 med_device_4 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:364(42.8%)
1:46(5.4%)
 851 (84.0%) 162 (16.0%)
109 med_device_5 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:308(36.2%)
1:102(12.0%)
 851 (84.0%) 162 (16.0%)
110 med_device_6 [numeric] Mean (sd) : -51.3 (49.5) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:406(47.7%)
1:4(0.5%)
 851 (84.0%) 162 (16.0%)
111 med_device_7 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:313(36.8%)
1:97(11.4%)
 851 (84.0%) 162 (16.0%)
112 med_device_8 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:311(36.5%)
1:99(11.6%)
 851 (84.0%) 162 (16.0%)
113 med_device_9 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:361(42.4%)
1:49(5.8%)
 851 (84.0%) 162 (16.0%)
114 med_device_10 [numeric] Mean (sd) : -51.3 (49.5) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:379(44.5%)
1:31(3.6%)
 851 (84.0%) 162 (16.0%)
115 med_device_11 [numeric] Mean (sd) : -51.3 (49.5) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:401(47.1%)
1:9(1.1%)
 851 (84.0%) 162 (16.0%)
116 other_med_device [character] 1. 0 2. glasses 3. Hh 4. no 5. No 6. Portable sleep 7. ventilator / re
2(25.0%)
1(12.5%)
1(12.5%)
1(12.5%)
1(12.5%)
1(12.5%)
1(12.5%)
 8 (0.8%) 1005 (99.2%)
117 emp_status [numeric] Mean (sd) : 1.8 (1.5) min < med < max: 1 < 1 < 7 IQR (CV) : 1.8 (0.8)
1:599(70.5%)
2:38(4.5%)
3:71(8.4%)
4:95(11.2%)
5:3(0.4%)
6:19(2.2%)
7:25(2.9%)
 850 (83.9%) 163 (16.1%)
118 emp_status_partner [numeric] Mean (sd) : 2.2 (1.8) min < med < max: 1 < 1 < 7 IQR (CV) : 2 (0.8)
1:515(60.6%)
2:52(6.1%)
3:80(9.4%)
4:125(14.7%)
5:5(0.6%)
6:15(1.8%)
7:58(6.8%)
 850 (83.9%) 163 (16.1%)
119 work_home [numeric] Mean (sd) : 0.3 (17.3) min < med < max: -99 < 3 < 4 IQR (CV) : 1 (58.1)
-99:25(2.9%)
3:568(66.9%)
4:256(30.2%)
 849 (83.8%) 164 (16.2%)
120 work_home_partner [numeric] Mean (sd) : -7.3 (28.2) min < med < max: -99 < 1 < 2 IQR (CV) : 1 (-3.9)
-99:73(8.6%)
1:489(57.6%)
2:287(33.8%)
 849 (83.8%) 164 (16.2%)
121 frontline [numeric] Mean (sd) : -0.9 (15.6) min < med < max: -99 < 2 < 2 IQR (CV) : 1 (-17.5)
-99:21(2.5%)
1:336(39.6%)
2:492(58.0%)
 849 (83.8%) 164 (16.2%)
122 frontline_partner [numeric] Mean (sd) : -6.6 (27.5) min < med < max: -99 < 2 < 2 IQR (CV) : 1 (-4.2)
-99:69(8.1%)
1:306(36.0%)
2:474(55.8%)
 849 (83.8%) 164 (16.2%)
123 hrs_work_3 [numeric] Mean (sd) : 31.8 (29.8) min < med < max: -99 < 39 < 74 IQR (CV) : 10 (0.9) 69 distinct values 848 (83.7%) 165 (16.3%)
124 hrs_work_4 [numeric] Mean (sd) : -35 (61.4) min < med < max: -99 < 0 < 80 IQR (CV) : 119 (-1.8) 65 distinct values 848 (83.7%) 165 (16.3%)
125 hrs_work_partner_3 [numeric] Mean (sd) : 18 (46.3) min < med < max: -99 < 35 < 80 IQR (CV) : 16 (2.6) 74 distinct values 848 (83.7%) 165 (16.3%)
126 hrs_work_partner_4 [numeric] Mean (sd) : -41.3 (61.9) min < med < max: -99 < -99 < 80 IQR (CV) : 116 (-1.5) 68 distinct values 848 (83.7%) 165 (16.3%)
127 min_commute_3 [numeric] Mean (sd) : 11.8 (43.5) min < med < max: -99 < 25 < 60 IQR (CV) : 20 (3.7) 59 distinct values 847 (83.6%) 166 (16.4%)
128 hrs_commute_3 [numeric] Mean (sd) : -42.7 (49.5) min < med < max: -99 < 0 < 4 IQR (CV) : 100 (-1.2)
-99:369(43.6%)
0:169(20.0%)
1:240(28.3%)
2:55(6.5%)
3:12(1.4%)
4:2(0.2%)
 847 (83.6%) 166 (16.4%)
129 min_commute_partner_3 [numeric] Mean (sd) : -0.3 (53.4) min < med < max: -99 < 21 < 60 IQR (CV) : 28.5 (-191.6) 56 distinct values 847 (83.6%) 166 (16.4%)
130 hrs_commute_partnet_3 [numeric] Mean (sd) : -49.5 (50) min < med < max: -99 < -99 < 4 IQR (CV) : 100 (-1)
-99:427(50.4%)
0:136(16.1%)
1:214(25.3%)
2:52(6.1%)
3:15(1.8%)
4:3(0.4%)
 847 (83.6%) 166 (16.4%)
131 rsn_nowork [numeric] Mean (sd) : -5.4 (27.8) min < med < max: -99 < 3 < 4 IQR (CV) : 1 (-5.1)
-99:20(8.1%)
1:8(3.2%)
2:50(20.2%)
3:144(58.3%)
4:25(10.1%)
 247 (24.4%) 766 (75.6%)
132 rsn_nowork_partner [numeric] Mean (sd) : -15.8 (39.4) min < med < max: -99 < 3 < 4 IQR (CV) : 1 (-2.5)
-99:60(18.3%)
1:5(1.5%)
2:76(23.2%)
3:154(47.0%)
4:33(10.1%)
 328 (32.4%) 685 (67.6%)
133 eip_1 [numeric] Mean (sd) : -4.3 (21.4) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-5)
-99:41(4.8%)
0:389(46.0%)
1:416(49.2%)
 846 (83.5%) 167 (16.5%)
134 eip_2 [numeric] Mean (sd) : -4.3 (21.4) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-5)
-99:41(4.8%)
0:388(45.9%)
1:417(49.3%)
 846 (83.5%) 167 (16.5%)
135 eip_3 [numeric] Mean (sd) : -4.5 (21.3) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-4.7)
-99:41(4.8%)
0:591(69.9%)
1:214(25.3%)
 846 (83.5%) 167 (16.5%)
136 eip_4 [numeric] Mean (sd) : -4.4 (21.4) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-4.9)
-99:41(4.8%)
0:451(53.3%)
1:354(41.8%)
 846 (83.5%) 167 (16.5%)
137 eip_5 [numeric] Mean (sd) : -4.4 (21.4) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-4.8)
-99:41(4.8%)
0:478(56.5%)
1:327(38.7%)
 846 (83.5%) 167 (16.5%)
138 eip_6 [numeric] Mean (sd) : -4.8 (21.3) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-4.4)
-99:41(4.8%)
0:794(93.9%)
1:11(1.3%)
 846 (83.5%) 167 (16.5%)
139 eip_7 [numeric] Mean (sd) : -4.8 (21.3) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-4.4)
-99:41(4.8%)
0:792(93.6%)
1:13(1.5%)
 846 (83.5%) 167 (16.5%)
140 other_eip [character] 1. 会 2. 0 3. 1 4. 1989 5. 200 6. 5000 7. Courses in hear 8. hui 9. no 10. shopping
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
2(18.2%)
1(9.1%)
 11 (1.1%) 1002 (98.9%)
141 year [numeric] Mean (sd) : 17.5 (7) min < med < max: 1 < 17 < 63 IQR (CV) : 8 (0.4) 42 distinct values 846 (83.5%) 167 (16.5%)
142 gender [character] 1. Female 2. Male 3. Other
286(33.8%)
550(65.1%)
9(1.1%)
 845 (83.4%) 168 (16.6%)
143 gender_7_TEXT [numeric] 1 distinct value
-99:845(100.0%)
 845 (83.4%) 168 (16.6%)
144 hispanic [numeric] Mean (sd) : 1.5 (0.5) min < med < max: 1 < 1 < 3 IQR (CV) : 1 (0.4)
1:425(50.4%)
2:404(47.9%)
3:15(1.8%)
 844 (83.3%) 169 (16.7%)
145 race [numeric] Mean (sd) : 1.4 (1) min < med < max: 1 < 1 < 8 IQR (CV) : 0 (0.7)
1:681(80.7%)
2:91(10.8%)
3:14(1.7%)
4:42(5.0%)
5:6(0.7%)
6:5(0.6%)
7:1(0.1%)
8:4(0.5%)
 844 (83.3%) 169 (16.7%)
146 education [numeric] Mean (sd) : 4.1 (6.3) min < med < max: -99 < 4 < 7 IQR (CV) : 3 (1.5)
-99:3(0.4%)
1:10(1.2%)
2:43(5.1%)
3:183(21.7%)
4:216(25.6%)
5:144(17.1%)
6:199(23.6%)
7:46(5.5%)
 844 (83.3%) 169 (16.7%)
147 marital [numeric] Mean (sd) : 2 (0.6) min < med < max: 1 < 2 < 6 IQR (CV) : 0 (0.3)
1:78(9.2%)
2:695(82.3%)
3:55(6.5%)
4:4(0.5%)
5:4(0.5%)
6:8(0.9%)
 844 (83.3%) 169 (16.7%)
148 depen [numeric] Min : 1 Mean : 1.2 Max : 2
1:650(77.0%)
2:194(23.0%)
 844 (83.3%) 169 (16.7%)
149 num_hsh [numeric] Mean (sd) : 3.7 (1.6) min < med < max: 0 < 4 < 10 IQR (CV) : 2 (0.4) 11 distinct values 844 (83.3%) 169 (16.7%)
150 num_children [numeric] Mean (sd) : 1.2 (1.7) min < med < max: 0 < 1 < 18 IQR (CV) : 0 (1.4) 11 distinct values 844 (83.3%) 169 (16.7%)
151 num_retired [numeric] Mean (sd) : 0.7 (0.9) min < med < max: 0 < 0 < 6 IQR (CV) : 1 (1.2)
0:447(53.0%)
1:196(23.2%)
2:189(22.4%)
3:4(0.5%)
4:5(0.6%)
5:2(0.2%)
6:1(0.1%)
 844 (83.3%) 169 (16.7%)
152 num_rooms [numeric] Mean (sd) : 4.4 (1.8) min < med < max: 0 < 4 < 22 IQR (CV) : 2 (0.4) 12 distinct values 844 (83.3%) 169 (16.7%)
153 num_bedrooms [numeric] Mean (sd) : 2.9 (1) min < med < max: 1 < 3 < 9 IQR (CV) : 1 (0.3)
1:42(5.0%)
2:237(28.1%)
3:359(42.6%)
4:145(17.2%)
5:49(5.8%)
6:8(1.0%)
7:1(0.1%)
9:1(0.1%)
 842 (83.1%) 171 (16.9%)
154 sq_ft [numeric] Mean (sd) : 5.3 (2.8) min < med < max: 1 < 5 < 13 IQR (CV) : 4 (0.5) 13 distinct values 844 (83.3%) 169 (16.7%)
155 hardships_1 [numeric] Mean (sd) : -11.4 (32.5) min < med < max: -99 < 1 < 1 IQR (CV) : 1 (-2.9)
-99:102(12.1%)
0:250(29.6%)
1:492(58.3%)
 844 (83.3%) 169 (16.7%)
156 hardships_2 [numeric] Mean (sd) : -11.8 (32.4) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.7)
-99:102(12.1%)
0:587(69.5%)
1:155(18.4%)
 844 (83.3%) 169 (16.7%)
157 hardships_3 [numeric] Mean (sd) : -11.7 (32.4) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.8)
-99:102(12.1%)
0:539(63.9%)
1:203(24.1%)
 844 (83.3%) 169 (16.7%)
158 hardships_4 [numeric] Mean (sd) : -11.7 (32.4) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.8)
-99:102(12.1%)
0:532(63.0%)
1:210(24.9%)
 844 (83.3%) 169 (16.7%)
159 hardships_5 [numeric] Mean (sd) : -11.8 (32.3) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.7)
-99:102(12.1%)
0:610(72.3%)
1:132(15.6%)
 844 (83.3%) 169 (16.7%)
160 hardships_6 [numeric] Mean (sd) : -11.9 (32.3) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.7)
-99:102(12.1%)
0:669(79.3%)
1:73(8.6%)
 844 (83.3%) 169 (16.7%)
161 welfare_1 [numeric] Mean (sd) : -9.2 (29.3) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-3.2)
-99:81(9.6%)
0:470(55.7%)
1:293(34.7%)
 844 (83.3%) 169 (16.7%)
162 welfare_2 [numeric] Mean (sd) : -9.2 (29.3) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-3.2)
-99:81(9.6%)
0:509(60.3%)
1:254(30.1%)
 844 (83.3%) 169 (16.7%)
163 welfare_3 [numeric] Mean (sd) : -9.4 (29.2) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-3.1)
-99:81(9.6%)
0:644(76.3%)
1:119(14.1%)
 844 (83.3%) 169 (16.7%)
164 welfare_4 [numeric] Mean (sd) : -9.1 (29.3) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-3.2)
-99:81(9.6%)
0:410(48.6%)
1:353(41.8%)
 844 (83.3%) 169 (16.7%)
165 welfare_5 [numeric] Mean (sd) : -9.3 (29.2) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-3.1)
-99:81(9.6%)
0:589(69.8%)
1:174(20.6%)
 844 (83.3%) 169 (16.7%)
166 welfare_6 [numeric] Mean (sd) : -9.4 (29.2) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-3.1)
-99:81(9.6%)
0:673(79.7%)
1:90(10.7%)
 844 (83.3%) 169 (16.7%)
167 welfare_7 [numeric] Mean (sd) : -9.4 (29.2) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-3.1)
-99:81(9.6%)
0:675(80.0%)
1:88(10.4%)
 844 (83.3%) 169 (16.7%)
168 email [character] Emails Valid Invalid Duplicates
839(99.9%)
1(0.1%)
34(4.0%)
 840 (82.9%) 173 (17.1%)
169 email_confirm [numeric] 1 distinct value
1:840(100.0%)
 840 (82.9%) 173 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.2)
2021-03-19

Part II.

Which responses to keep?

  1. Consider odd response those outside the “normal” range of duration (1.36, 71) AND above 91 NA values
  2. Summary table: responses that Agree to participate, valid zip code (78202 or 78230), not an odd response
### Duration and number of NAs
kable(table(san$nas_out, san$duration_min_out), caption = "NAs (%) vs Duration (min)") %>% 
  kable_classic(full_width = F) %>% 
  footnote(general = "NAs in (10%, 90%); duration in (2.5%, 97.5%)")
NAs (%) vs Duration (min)
Spend too much time Normal Spend too little time
Above 90% quantile 4 80 33
Normal 19 764 0
Below 10% quantile 3 110 0
Note:
NAs in (10%, 90%); duration in (2.5%, 97.5%) 
kable(table(san$nas_out, san$duration_min_out2), caption = "NAs (%) vs Duration (min)") %>% 
  kable_classic(full_width = F) %>% 
  footnote(general = "NAs in (10%, 90%); duration in (5%, 95%)")
NAs (%) vs Duration (min)
Above 95% quantile Normal Below 5% quantile
Above 90% quantile 5 63 49
Normal 42 739 2
Below 10% quantile 4 109 0
Note:
NAs in (10%, 90%); duration in (5%, 95%) 
kable(table(san$odd_r2), caption = "Odd responses") %>% 
  kable_classic(full_width = F)
Odd responses
Var1 Freq
Normal 959
Odd response 54 
sank <- san %>% 
  # consent Agree and double check on survey participation. Keep only zip codes 78202 or 78230. And keep only the first answer for unique emails
  filter(consent == "1" | disagree == "1", zipcode != 3, odd_r2 == "Normal") %>% 
  mutate(zipcode = if_else(zipcode == "1", "Elm Creek", 
                           if_else(zipcode == "2", "Jefferson Heights", NA_character_)),
         rep_email = if_else(email == lag(email, n = 1, order_by = email), "rep_email", "first_entry")) %>% 
  filter(rep_email == "first_entry")

#view(dfSummary(sank[, 5:173], plain.ascii = F, graph.magnif = .75, labels.col = T, max.string.width = 15), method = "render")

Section IV. Background Characteristics

sank %>%
  select(146, 148:171, 175, 178:189) %>% 
  tbl_summary(by = duration_min_out, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section IV. Background characteristics (N = {N})**") %>% 
  bold_labels()
variable N Spend too much time, N = 131 Normal, N = 7921 Spend too little time, N = 01 p-value2
gender 805 0.6
Female 3 (23%) 273 (34%) 0 (NA%)
Male 10 (77%) 510 (64%) 0 (NA%)
Other 0 (0%) 9 (1.1%) 0 (NA%)
hispanic 805 0.8
1 6 (46%) 403 (51%) 0 (NA%)
2 7 (54%) 374 (47%) 0 (NA%)
3 0 (0%) 15 (1.9%) 0 (NA%)
race 805 0.7
1 13 (100%) 634 (80%) 0 (NA%)
2 0 (0%) 90 (11%) 0 (NA%)
3 0 (0%) 13 (1.6%) 0 (NA%)
4 0 (0%) 39 (4.9%) 0 (NA%)
5 0 (0%) 6 (0.8%) 0 (NA%)
6 0 (0%) 5 (0.6%) 0 (NA%)
7 0 (0%) 1 (0.1%) 0 (NA%)
8 0 (0%) 4 (0.5%) 0 (NA%)
education 805 0.4
-99 0 (0%) 3 (0.4%) 0 (NA%)
1 0 (0%) 10 (1.3%) 0 (NA%)
2 1 (7.7%) 38 (4.8%) 0 (NA%)
3 1 (7.7%) 174 (22%) 0 (NA%)
4 5 (38%) 203 (26%) 0 (NA%)
5 1 (7.7%) 136 (17%) 0 (NA%)
6 3 (23%) 188 (24%) 0 (NA%)
7 2 (15%) 40 (5.1%) 0 (NA%)
marital 805 >0.9
1 1 (7.7%) 75 (9.5%) 0 (NA%)
2 12 (92%) 648 (82%) 0 (NA%)
3 0 (0%) 53 (6.7%) 0 (NA%)
4 0 (0%) 4 (0.5%) 0 (NA%)
5 0 (0%) 4 (0.5%) 0 (NA%)
6 0 (0%) 8 (1.0%) 0 (NA%)
depen 805 0.7
1 11 (85%) 603 (76%) 0 (NA%)
2 2 (15%) 189 (24%) 0 (NA%)
num_hsh 805 3.5 (1.5) 3.7 (1.5) NA (NA) 0.8
num_children 805 1.5 (0.9) 1.2 (1.8) NA (NA) 0.061
num_retired 805 0.4
0 10 (77%) 426 (54%) 0 (NA%)
1 1 (7.7%) 188 (24%) 0 (NA%)
2 2 (15%) 167 (21%) 0 (NA%)
3 0 (0%) 4 (0.5%) 0 (NA%)
4 0 (0%) 5 (0.6%) 0 (NA%)
5 0 (0%) 2 (0.3%) 0 (NA%)
num_rooms 805 4.8 (1.0) 4.3 (1.6) NA (NA) 0.2
num_bedrooms 803 0.4
1 0 (0%) 41 (5.2%) 0 (NA%)
2 2 (15%) 227 (29%) 0 (NA%)
3 5 (38%) 340 (43%) 0 (NA%)
4 5 (38%) 129 (16%) 0 (NA%)
5 1 (7.7%) 44 (5.6%) 0 (NA%)
6 0 (0%) 7 (0.9%) 0 (NA%)
7 0 (0%) 1 (0.1%) 0 (NA%)
9 0 (0%) 1 (0.1%) 0 (NA%)
Unknown 0 2 0
sq_ft 805 7.4 (2.6) 5.2 (2.7) NA (NA) 0.005
hardships_1 805 0.13
-99 1 (7.7%) 100 (13%) 0 (NA%)
0 1 (7.7%) 241 (30%) 0 (NA%)
1 11 (85%) 451 (57%) 0 (NA%)
hardships_2 805 0.2
-99 1 (7.7%) 100 (13%) 0 (NA%)
0 12 (92%) 542 (68%) 0 (NA%)
1 0 (0%) 150 (19%) 0 (NA%)
hardships_3 805 0.8
-99 1 (7.7%) 100 (13%) 0 (NA%)
0 10 (77%) 505 (64%) 0 (NA%)
1 2 (15%) 187 (24%) 0 (NA%)
hardships_4 805 0.6
-99 1 (7.7%) 100 (13%) 0 (NA%)
0 10 (77%) 490 (62%) 0 (NA%)
1 2 (15%) 202 (26%) 0 (NA%)
hardships_5 805 0.8
-99 1 (7.7%) 100 (13%) 0 (NA%)
0 11 (85%) 566 (71%) 0 (NA%)
1 1 (7.7%) 126 (16%) 0 (NA%)
hardships_6 805 0.2
-99 1 (7.7%) 100 (13%) 0 (NA%)
0 9 (69%) 623 (79%) 0 (NA%)
1 3 (23%) 69 (8.7%) 0 (NA%)
welfare_1 805 0.006
-99 0 (0%) 80 (10%) 0 (NA%)
0 3 (23%) 458 (58%) 0 (NA%)
1 10 (77%) 254 (32%) 0 (NA%)
welfare_2 805 0.3
-99 0 (0%) 80 (10%) 0 (NA%)
0 7 (54%) 472 (60%) 0 (NA%)
1 6 (46%) 240 (30%) 0 (NA%)
welfare_3 805 0.7
-99 0 (0%) 80 (10%) 0 (NA%)
0 11 (85%) 605 (76%) 0 (NA%)
1 2 (15%) 107 (14%) 0 (NA%)
welfare_4 805 0.11
-99 0 (0%) 80 (10%) 0 (NA%)
0 10 (77%) 374 (47%) 0 (NA%)
1 3 (23%) 338 (43%) 0 (NA%)
welfare_5 805 0.4
-99 0 (0%) 80 (10%) 0 (NA%)
0 9 (69%) 551 (70%) 0 (NA%)
1 4 (31%) 161 (20%) 0 (NA%)
welfare_6 805 0.3
-99 0 (0%) 80 (10%) 0 (NA%)
0 13 (100%) 624 (79%) 0 (NA%)
1 0 (0%) 88 (11%) 0 (NA%)
welfare_7 805 0.7
-99 0 (0%) 80 (10%) 0 (NA%)
0 12 (92%) 627 (79%) 0 (NA%)
1 1 (7.7%) 85 (11%) 0 (NA%)
number_missing_value 805 15.8 (4.4) 16.9 (6.5) NA (NA) 0.7
upper_out 805
97.3399999999999 13 (100%) 792 (100%) 0 (NA%)
upper_out2 805
71.24 13 (100%) 792 (100%) 0 (NA%)
lower_out 805
0.7 13 (100%) 792 (100%) 0 (NA%)
lower_out2 805
1.36 13 (100%) 792 (100%) 0 (NA%)
na_low 805
5.7 13 (100%) 792 (100%) 0 (NA%)
na_upp 805
90.8 13 (100%) 792 (100%) 0 (NA%)
duration_min_out2 805 <0.001
Above 95% quantile 13 (100%) 20 (2.5%) 0 (NA%)
Normal 0 (0%) 772 (97%) 0 (NA%)
Below 5% quantile 0 (0%) 0 (0%) 0 (NA%)
nas_out 805 0.2
Above 90% quantile 0 (0%) 0 (0%) 0 (NA%)
Normal 10 (77%) 691 (87%) 0 (NA%)
Below 10% quantile 3 (23%) 101 (13%) 0 (NA%)
odd_r 805
Normal 13 (100%) 792 (100%) 0 (NA%)
odd_r2 805
Normal 13 (100%) 792 (100%) 0 (NA%)
yrbr 805 1,985.5 (6.1) 1,982.5 (7.0) NA (NA) 0.2

1 n (%); Mean (SD)

2 Fisher's exact test; Wilcoxon rank sum test

Section Ia. Time Use

sank %>%
  select(16:80, 175, 178:189) %>% 
  tbl_summary(by = duration_min_out, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section Ia. Time Use (N = {N})**") %>% 
  bold_labels()
variable N Spend too much time, N = 131 Normal, N = 7921 Spend too little time, N = 01 p-value2
zipcode 805 0.3
Elm Creek 10 (77%) 469 (59%) 0 (NA%)
Jefferson Heights 3 (23%) 323 (41%) 0 (NA%)
per_care_1 805 8.2 (0.9) 8.0 (1.7) NA (NA) 0.6
per_care_2 805 -5.3 (28.2) 2.1 (5.5) NA (NA) 0.2
per_care_3 805 -6.0 (28.0) -0.6 (16.0) NA (NA) 0.8
per_care_4 805 -6.6 (27.8) 0.2 (12.5) NA (NA) 0.12
per_care_wknd_1 805 0.2
4 3 (23%) 349 (44%) 0 (NA%)
5 10 (77%) 443 (56%) 0 (NA%)
per_care_wknd_2 803 0.4
4 4 (33%) 396 (50%) 0 (NA%)
5 8 (67%) 395 (50%) 0 (NA%)
Unknown 1 1 0
per_care_wknd_3 785 0.6
4 8 (67%) 421 (54%) 0 (NA%)
5 4 (33%) 352 (46%) 0 (NA%)
Unknown 1 19 0
per_care_wknd_4 792 0.14
4 10 (83%) 465 (60%) 0 (NA%)
5 2 (17%) 315 (40%) 0 (NA%)
Unknown 1 12 0
pc_wknd_time_1 453 9.6 (1.1) 9.3 (2.0) NA (NA) 0.5
Unknown 3 349 0
pc_wknd_time_2 403 3.9 (0.4) 3.8 (3.0) NA (NA) 0.076
Unknown 5 397 0
pc_wknd_time_3 356 2.3 (0.8) 2.5 (6.0) NA (NA) 0.7
Unknown 9 440 0
pc_wknd_time_4 317 2.1 (1.3) 2.4 (2.7) NA (NA) 0.7
Unknown 11 477 0
per_care_covid_1 805 0.10
1 0 (0%) 30 (3.8%) 0 (NA%)
2 1 (7.7%) 57 (7.2%) 0 (NA%)
3 2 (15%) 323 (41%) 0 (NA%)
4 8 (62%) 217 (27%) 0 (NA%)
5 2 (15%) 165 (21%) 0 (NA%)
per_care_covid_2 805 0.5
1 0 (0%) 2 (0.3%) 0 (NA%)
2 0 (0%) 80 (10%) 0 (NA%)
3 7 (54%) 372 (47%) 0 (NA%)
4 3 (23%) 235 (30%) 0 (NA%)
5 3 (23%) 103 (13%) 0 (NA%)
per_care_covid_3 805 0.2
1 0 (0%) 9 (1.1%) 0 (NA%)
2 3 (23%) 57 (7.2%) 0 (NA%)
3 4 (31%) 403 (51%) 0 (NA%)
4 3 (23%) 208 (26%) 0 (NA%)
5 3 (23%) 110 (14%) 0 (NA%)
6 0 (0%) 5 (0.6%) 0 (NA%)
per_care_covid_4 805 0.6
1 0 (0%) 17 (2.1%) 0 (NA%)
2 0 (0%) 86 (11%) 0 (NA%)
3 9 (69%) 401 (51%) 0 (NA%)
4 2 (15%) 207 (26%) 0 (NA%)
5 2 (15%) 77 (9.7%) 0 (NA%)
6 0 (0%) 4 (0.5%) 0 (NA%)
st_act_1 805 -10.7 (39.2) -19.3 (44.6) NA (NA) 0.5
st_act_2 805 -21.1 (44.4) -26.0 (45.9) NA (NA) 0.4
st_act_3 805 -29.9 (48.0) -30.6 (47.5) NA (NA) 0.3
st_act_wknd_1 622 0.4
3 4 (33%) 304 (50%) 0 (NA%)
4 8 (67%) 306 (50%) 0 (NA%)
Unknown 1 182 0
st_act_wknd_2 583 0.8
3 4 (40%) 273 (48%) 0 (NA%)
4 6 (60%) 300 (52%) 0 (NA%)
Unknown 3 219 0
st_act_wknd_3 547 0.5
3 6 (67%) 274 (51%) 0 (NA%)
4 3 (33%) 264 (49%) 0 (NA%)
Unknown 4 254 0
sa_wknd_time_1 315 2.8 (1.0) -0.9 (20.8) NA (NA) >0.9
Unknown 5 485 0
sa_wknd_time_2 306 1.0 (0.5) 2.7 (10.8) NA (NA) 0.004
Unknown 7 492 0
sa_wknd_time_3 269 -24.6 (49.6) -13.0 (37.0) NA (NA) 0.042
Unknown 9 527 0
st_act_covid_1 805 0.036
1 0 (0%) 39 (4.9%) 0 (NA%)
2 7 (54%) 140 (18%) 0 (NA%)
3 1 (7.7%) 279 (35%) 0 (NA%)
4 2 (15%) 119 (15%) 0 (NA%)
5 1 (7.7%) 74 (9.3%) 0 (NA%)
6 2 (15%) 141 (18%) 0 (NA%)
st_act_covid_2 805 0.075
1 1 (7.7%) 25 (3.2%) 0 (NA%)
2 5 (38%) 133 (17%) 0 (NA%)
3 1 (7.7%) 251 (32%) 0 (NA%)
4 2 (15%) 171 (22%) 0 (NA%)
5 2 (15%) 64 (8.1%) 0 (NA%)
6 2 (15%) 148 (19%) 0 (NA%)
st_act_covid_3 805 >0.9
1 2 (15%) 102 (13%) 0 (NA%)
2 1 (7.7%) 123 (16%) 0 (NA%)
3 5 (38%) 259 (33%) 0 (NA%)
4 2 (15%) 96 (12%) 0 (NA%)
5 0 (0%) 36 (4.5%) 0 (NA%)
6 3 (23%) 176 (22%) 0 (NA%)
own_device 805 0.6
1 0 (0%) 92 (12%) 0 (NA%)
2 0 (0%) 45 (5.7%) 0 (NA%)
3 13 (100%) 644 (81%) 0 (NA%)
4 0 (0%) 11 (1.4%) 0 (NA%)
dev_act_1 794 -5.6 (28.1) -2.0 (22.1) NA (NA) 0.085
Unknown 0 11 0
dev_act_7 794 2.1 (1.9) 0.2 (17.1) NA (NA) 0.090
Unknown 0 11 0
dev_act_3 794 -14.4 (37.6) -13.2 (35.9) NA (NA) 0.11
Unknown 0 11 0
dev_act_6 794 -14.0 (37.7) -11.3 (34.9) NA (NA) 0.2
Unknown 0 11 0
dev_act_wknd_1 755 0.082
2 10 (83%) 425 (57%) 0 (NA%)
3 2 (17%) 318 (43%) 0 (NA%)
Unknown 1 49 0
dev_act_wknd_2 772 0.6
2 9 (69%) 437 (58%) 0 (NA%)
3 4 (31%) 322 (42%) 0 (NA%)
Unknown 0 33 0
dev_act_wknd_3 677 0.6
2 6 (55%) 302 (45%) 0 (NA%)
3 5 (45%) 364 (55%) 0 (NA%)
Unknown 2 126 0
dev_act_wknd_4 687 >0.9
2 7 (64%) 416 (62%) 0 (NA%)
3 4 (36%) 260 (38%) 0 (NA%)
Unknown 2 116 0
da_wknd_time_1 435 3.6 (2.5) 3.4 (5.3) NA (NA) 0.5
Unknown 3 367 0
da_wknd_time_2 446 2.3 (1.2) 3.5 (5.6) NA (NA) 0.10
Unknown 4 355 0
da_wknd_time_3 308 2.4 (2.6) 1.8 (10.5) NA (NA) 0.8
Unknown 7 490 0
da_wknd_time_6 423 3.2 (2.5) 2.4 (11.5) NA (NA) 0.6
Unknown 6 376 0
dev_act_covid_1 794 0.8
1 0 (0%) 22 (2.8%) 0 (NA%)
2 0 (0%) 61 (7.8%) 0 (NA%)
3 3 (23%) 260 (33%) 0 (NA%)
4 6 (46%) 266 (34%) 0 (NA%)
5 4 (31%) 158 (20%) 0 (NA%)
6 0 (0%) 14 (1.8%) 0 (NA%)
Unknown 0 11 0
dev_act_covid_2 794 0.5
1 0 (0%) 10 (1.3%) 0 (NA%)
2 0 (0%) 107 (14%) 0 (NA%)
3 4 (31%) 246 (31%) 0 (NA%)
4 5 (38%) 289 (37%) 0 (NA%)
5 4 (31%) 120 (15%) 0 (NA%)
6 0 (0%) 9 (1.2%) 0 (NA%)
Unknown 0 11 0
dev_act_covid_3 794 0.6
1 0 (0%) 17 (2.2%) 0 (NA%)
2 2 (15%) 82 (10%) 0 (NA%)
3 4 (31%) 329 (42%) 0 (NA%)
4 5 (38%) 231 (30%) 0 (NA%)
5 1 (7.7%) 98 (13%) 0 (NA%)
6 1 (7.7%) 24 (3.1%) 0 (NA%)
Unknown 0 11 0
dev_act_covid_4 794 0.6
1 0 (0%) 7 (0.9%) 0 (NA%)
2 0 (0%) 62 (7.9%) 0 (NA%)
3 4 (31%) 318 (41%) 0 (NA%)
4 7 (54%) 271 (35%) 0 (NA%)
5 2 (15%) 117 (15%) 0 (NA%)
6 0 (0%) 6 (0.8%) 0 (NA%)
Unknown 0 11 0
dev_act_covid_5 794 >0.9
1 0 (0%) 11 (1.4%) 0 (NA%)
2 0 (0%) 51 (6.5%) 0 (NA%)
3 5 (38%) 287 (37%) 0 (NA%)
4 6 (46%) 297 (38%) 0 (NA%)
5 2 (15%) 129 (17%) 0 (NA%)
6 0 (0%) 6 (0.8%) 0 (NA%)
Unknown 0 11 0
dev_act_covid_6 794 0.5
1 0 (0%) 9 (1.2%) 0 (NA%)
2 0 (0%) 83 (11%) 0 (NA%)
3 5 (38%) 324 (41%) 0 (NA%)
4 7 (54%) 244 (31%) 0 (NA%)
5 1 (7.7%) 112 (14%) 0 (NA%)
6 0 (0%) 9 (1.2%) 0 (NA%)
Unknown 0 11 0
dev_act_covid_7 794 0.7
1 0 (0%) 15 (1.9%) 0 (NA%)
2 0 (0%) 50 (6.4%) 0 (NA%)
3 2 (15%) 230 (29%) 0 (NA%)
4 7 (54%) 305 (39%) 0 (NA%)
5 3 (23%) 142 (18%) 0 (NA%)
6 1 (7.7%) 39 (5.0%) 0 (NA%)
Unknown 0 11 0
other_act_1 805 1.7 (2.3) -12.2 (36.1) NA (NA) 0.2
other_act_2 805 -6.9 (27.7) -3.0 (21.7) NA (NA) 0.006
other_act_3 805 -7.0 (27.6) -2.6 (20.5) NA (NA) <0.001
other_act_4 805 -45.3 (51.8) -23.9 (44.0) NA (NA) 0.018
other_act_wknd_1 690 0.8
2 6 (46%) 283 (42%) 0 (NA%)
3 7 (54%) 394 (58%) 0 (NA%)
Unknown 0 115 0
other_act_wknd_2 767 0.6
2 8 (67%) 433 (57%) 0 (NA%)
3 4 (33%) 322 (43%) 0 (NA%)
Unknown 1 37 0
other_act_wknd_3 770 0.077
2 9 (75%) 351 (46%) 0 (NA%)
3 3 (25%) 407 (54%) 0 (NA%)
Unknown 1 34 0
other_act_wknd_4 601 >0.9
2 3 (43%) 285 (48%) 0 (NA%)
3 4 (57%) 309 (52%) 0 (NA%)
Unknown 6 198 0
oa_wknd_time_1 532 2.6 (2.5) 2.7 (8.2) NA (NA) 0.4
Unknown 2 271 0
oa_wknd_time_2 567 1.4 (1.0) 1.8 (8.9) NA (NA) 0.038
Unknown 3 235 0
oa_wknd_time_3 570 1.1 (1.3) 1.5 (8.9) NA (NA) 0.003
Unknown 2 233 0
oa_wknd_time_4 463 1.0 (1.0) 0.1 (14.3) NA (NA) 0.2
Unknown 7 335 0
other_act_covid_1 805 >0.9
1 0 (0%) 25 (3.2%) 0 (NA%)
2 0 (0%) 70 (8.8%) 0 (NA%)
3 7 (54%) 356 (45%) 0 (NA%)
4 4 (31%) 200 (25%) 0 (NA%)
5 2 (15%) 118 (15%) 0 (NA%)
6 0 (0%) 23 (2.9%) 0 (NA%)
other_act_covid_2 805 0.055
1 0 (0%) 12 (1.5%) 0 (NA%)
2 1 (7.7%) 75 (9.5%) 0 (NA%)
3 1 (7.7%) 293 (37%) 0 (NA%)
4 10 (77%) 264 (33%) 0 (NA%)
5 1 (7.7%) 139 (18%) 0 (NA%)
6 0 (0%) 9 (1.1%) 0 (NA%)
other_act_covid_3 805 0.3
1 0 (0%) 12 (1.5%) 0 (NA%)
2 1 (7.7%) 64 (8.1%) 0 (NA%)
3 7 (54%) 340 (43%) 0 (NA%)
4 4 (31%) 244 (31%) 0 (NA%)
5 0 (0%) 119 (15%) 0 (NA%)
6 1 (7.7%) 13 (1.6%) 0 (NA%)
other_act_covid_4 805 0.015
1 2 (15%) 24 (3.0%) 0 (NA%)
2 1 (7.7%) 68 (8.6%) 0 (NA%)
3 6 (46%) 354 (45%) 0 (NA%)
4 0 (0%) 220 (28%) 0 (NA%)
5 2 (15%) 71 (9.0%) 0 (NA%)
6 2 (15%) 55 (6.9%) 0 (NA%)
number_missing_value 805 15.8 (4.4) 16.9 (6.5) NA (NA) 0.7
upper_out 805
97.3399999999999 13 (100%) 792 (100%) 0 (NA%)
upper_out2 805
71.24 13 (100%) 792 (100%) 0 (NA%)
lower_out 805
0.7 13 (100%) 792 (100%) 0 (NA%)
lower_out2 805
1.36 13 (100%) 792 (100%) 0 (NA%)
na_low 805
5.7 13 (100%) 792 (100%) 0 (NA%)
na_upp 805
90.8 13 (100%) 792 (100%) 0 (NA%)
duration_min_out2 805 <0.001
Above 95% quantile 13 (100%) 20 (2.5%) 0 (NA%)
Normal 0 (0%) 772 (97%) 0 (NA%)
Below 5% quantile 0 (0%) 0 (0%) 0 (NA%)
nas_out 805 0.2
Above 90% quantile 0 (0%) 0 (0%) 0 (NA%)
Normal 10 (77%) 691 (87%) 0 (NA%)
Below 10% quantile 3 (23%) 101 (13%) 0 (NA%)
odd_r 805
Normal 13 (100%) 792 (100%) 0 (NA%)
odd_r2 805
Normal 13 (100%) 792 (100%) 0 (NA%)
yrbr 805 1,985.5 (6.1) 1,982.5 (7.0) NA (NA) 0.2

1 n (%); Mean (SD)

2 Fisher's exact test; Wilcoxon rank sum test

Section Ib. Energy Use

sank %>%
  select(81:87, 175, 178:189) %>% 
  tbl_summary(by = duration_min_out, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section Ib. Energy Use (N = {N})**") %>% 
  bold_labels()
variable N Spend too much time, N = 131 Normal, N = 7921 Spend too little time, N = 01 p-value2
pre_electric 805 105.6 (72.2) 173.9 (476.3) NA (NA) 0.8
post_electric 805 422.5 (1,071.5) 163.5 (257.5) NA (NA) 0.9
pre_gas 805 142.8 (75.9) 215.8 (479.3) NA (NA) 0.4
post_gas 805 71.8 (39.2) 174.6 (506.9) NA (NA) 0.2
num_vehicles 805 >0.9
0 0 (0%) 26 (3.3%) 0 (NA%)
1 8 (62%) 423 (53%) 0 (NA%)
2 5 (38%) 313 (40%) 0 (NA%)
3 0 (0%) 28 (3.5%) 0 (NA%)
5 0 (0%) 1 (0.1%) 0 (NA%)
20 0 (0%) 1 (0.1%) 0 (NA%)
pre_trans 805 249.6 (221.4) 372.0 (598.7) NA (NA) 0.7
post_trans 805 85.4 (45.3) 223.6 (627.0) NA (NA) 0.5
number_missing_value 805 15.8 (4.4) 16.9 (6.5) NA (NA) 0.7
upper_out 805
97.3399999999999 13 (100%) 792 (100%) 0 (NA%)
upper_out2 805
71.24 13 (100%) 792 (100%) 0 (NA%)
lower_out 805
0.7 13 (100%) 792 (100%) 0 (NA%)
lower_out2 805
1.36 13 (100%) 792 (100%) 0 (NA%)
na_low 805
5.7 13 (100%) 792 (100%) 0 (NA%)
na_upp 805
90.8 13 (100%) 792 (100%) 0 (NA%)
duration_min_out2 805 <0.001
Above 95% quantile 13 (100%) 20 (2.5%) 0 (NA%)
Normal 0 (0%) 772 (97%) 0 (NA%)
Below 5% quantile 0 (0%) 0 (0%) 0 (NA%)
nas_out 805 0.2
Above 90% quantile 0 (0%) 0 (0%) 0 (NA%)
Normal 10 (77%) 691 (87%) 0 (NA%)
Below 10% quantile 3 (23%) 101 (13%) 0 (NA%)
odd_r 805
Normal 13 (100%) 792 (100%) 0 (NA%)
odd_r2 805
Normal 13 (100%) 792 (100%) 0 (NA%)
yrbr 805 1,985.5 (6.1) 1,982.5 (7.0) NA (NA) 0.2

1 Mean (SD); n (%)

2 Wilcoxon rank sum test; Fisher's exact test

Section II. Health Conditions

sank %>%
  select(88:101, 103, 105:119, 175, 178:189) %>% 
  tbl_summary(by = duration_min_out, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section II. Health Conditions (N = {N})**") %>% 
  bold_labels()
variable N Spend too much time, N = 131 Normal, N = 7921 Spend too little time, N = 01 p-value2
health 805 0.7
Excellent 1 (7.7%) 129 (16%) 0 (NA%)
Very good 5 (38%) 321 (41%) 0 (NA%)
Good 7 (54%) 291 (37%) 0 (NA%)
Fair 0 (0%) 41 (5.2%) 0 (NA%)
Poor 0 (0%) 10 (1.3%) 0 (NA%)
mental_health_anxious 805 >0.9
-99 0 (0%) 18 (2.3%) 0 (NA%)
1 3 (23%) 231 (29%) 0 (NA%)
2 6 (46%) 286 (36%) 0 (NA%)
3 3 (23%) 197 (25%) 0 (NA%)
4 1 (7.7%) 60 (7.6%) 0 (NA%)
mental_health_worry 805 >0.9
-99 0 (0%) 24 (3.0%) 0 (NA%)
1 5 (38%) 305 (39%) 0 (NA%)
2 5 (38%) 230 (29%) 0 (NA%)
3 3 (23%) 182 (23%) 0 (NA%)
4 0 (0%) 51 (6.4%) 0 (NA%)
mental_health_interest 805 >0.9
-99 0 (0%) 20 (2.5%) 0 (NA%)
1 4 (31%) 215 (27%) 0 (NA%)
2 5 (38%) 301 (38%) 0 (NA%)
3 4 (31%) 195 (25%) 0 (NA%)
4 0 (0%) 61 (7.7%) 0 (NA%)
mental_health_down 805 >0.9
-99 0 (0%) 26 (3.3%) 0 (NA%)
1 6 (46%) 302 (38%) 0 (NA%)
2 3 (23%) 212 (27%) 0 (NA%)
3 4 (31%) 194 (24%) 0 (NA%)
4 0 (0%) 58 (7.3%) 0 (NA%)
physical_health 805 3.8 (3.5) 3.0 (3.4) NA (NA) 0.2
mental_health 805 11.1 (8.1) 3.8 (4.6) NA (NA) <0.001
pre_phy_health 805 0.8
-99 0 (0%) 5 (0.6%) 0 (NA%)
1 1 (7.7%) 73 (9.2%) 0 (NA%)
2 10 (77%) 505 (64%) 0 (NA%)
3 2 (15%) 209 (26%) 0 (NA%)
pre_mental_health 805 0.6
-99 0 (0%) 5 (0.6%) 0 (NA%)
1 4 (31%) 152 (19%) 0 (NA%)
2 6 (46%) 441 (56%) 0 (NA%)
3 3 (23%) 194 (24%) 0 (NA%)
days_poor_health 805 1.5 (2.0) 3.1 (4.4) NA (NA) 0.2
impairment 805 >0.9
-99 0 (0%) 41 (5.2%) 0 (NA%)
1 4 (31%) 260 (33%) 0 (NA%)
2 9 (69%) 491 (62%) 0 (NA%)
impairment_hsh...103 805 0.4
-99 0 (0%) 54 (6.8%) 0 (NA%)
1 2 (15%) 249 (31%) 0 (NA%)
2 11 (85%) 489 (62%) 0 (NA%)
num_hsh_impair 805 0.4
0 9 (69%) 479 (60%) 0 (NA%)
1 1 (7.7%) 203 (26%) 0 (NA%)
2 3 (23%) 89 (11%) 0 (NA%)
3 0 (0%) 11 (1.4%) 0 (NA%)
4 0 (0%) 6 (0.8%) 0 (NA%)
5 0 (0%) 1 (0.1%) 0 (NA%)
6 0 (0%) 2 (0.3%) 0 (NA%)
10 0 (0%) 1 (0.1%) 0 (NA%)
major_impairment 264 3.0 (2.7) -7.3 (38.3) NA (NA) 0.2
Unknown 9 532 0
impairment_hsh...107 251 4.0 (4.2) -8.1 (38.9) NA (NA) 0.4
Unknown 11 543 0
days_impairment_1 805 -50.7 (54.4) -30.8 (50.8) NA (NA) 0.2
weeks_impairment_4 805 0.7
-99 11 (85%) 424 (54%) 0 (NA%)
0 1 (7.7%) 59 (7.4%) 0 (NA%)
1 1 (7.7%) 141 (18%) 0 (NA%)
2 0 (0%) 80 (10%) 0 (NA%)
3 0 (0%) 47 (5.9%) 0 (NA%)
4 0 (0%) 24 (3.0%) 0 (NA%)
5 0 (0%) 9 (1.1%) 0 (NA%)
6 0 (0%) 4 (0.5%) 0 (NA%)
7 0 (0%) 4 (0.5%) 0 (NA%)
months_impairment_1 805 -83.7 (37.4) -59.2 (49.4) NA (NA) 0.056
years_impairment_1 805 >0.9
-99 11 (85%) 502 (63%) 0 (NA%)
0 1 (7.7%) 88 (11%) 0 (NA%)
1 1 (7.7%) 94 (12%) 0 (NA%)
2 0 (0%) 56 (7.1%) 0 (NA%)
3 0 (0%) 21 (2.7%) 0 (NA%)
4 0 (0%) 15 (1.9%) 0 (NA%)
5 0 (0%) 9 (1.1%) 0 (NA%)
6 0 (0%) 3 (0.4%) 0 (NA%)
7 0 (0%) 4 (0.5%) 0 (NA%)
med_device_1 805 0.5
-99 9 (69%) 404 (51%) 0 (NA%)
0 2 (15%) 235 (30%) 0 (NA%)
1 2 (15%) 153 (19%) 0 (NA%)
med_device_2 805 0.4
-99 9 (69%) 404 (51%) 0 (NA%)
0 3 (23%) 317 (40%) 0 (NA%)
1 1 (7.7%) 71 (9.0%) 0 (NA%)
med_device_3 805 0.5
-99 9 (69%) 404 (51%) 0 (NA%)
0 4 (31%) 330 (42%) 0 (NA%)
1 0 (0%) 58 (7.3%) 0 (NA%)
med_device_4 805 0.6
-99 9 (69%) 404 (51%) 0 (NA%)
0 4 (31%) 343 (43%) 0 (NA%)
1 0 (0%) 45 (5.7%) 0 (NA%)
med_device_5 805 0.5
-99 9 (69%) 404 (51%) 0 (NA%)
0 3 (23%) 295 (37%) 0 (NA%)
1 1 (7.7%) 93 (12%) 0 (NA%)
med_device_6 805 0.3
-99 9 (69%) 404 (51%) 0 (NA%)
0 4 (31%) 386 (49%) 0 (NA%)
1 0 (0%) 2 (0.3%) 0 (NA%)
med_device_7 805 0.4
-99 9 (69%) 404 (51%) 0 (NA%)
0 4 (31%) 306 (39%) 0 (NA%)
1 0 (0%) 82 (10%) 0 (NA%)
med_device_8 805 0.2
-99 9 (69%) 404 (51%) 0 (NA%)
0 2 (15%) 295 (37%) 0 (NA%)
1 2 (15%) 93 (12%) 0 (NA%)
med_device_9 805 0.5
-99 9 (69%) 404 (51%) 0 (NA%)
0 4 (31%) 345 (44%) 0 (NA%)
1 0 (0%) 43 (5.4%) 0 (NA%)
med_device_10 805 0.6
-99 9 (69%) 404 (51%) 0 (NA%)
0 4 (31%) 358 (45%) 0 (NA%)
1 0 (0%) 30 (3.8%) 0 (NA%)
med_device_11 805 0.4
-99 9 (69%) 404 (51%) 0 (NA%)
0 4 (31%) 380 (48%) 0 (NA%)
1 0 (0%) 8 (1.0%) 0 (NA%)
number_missing_value 805 15.8 (4.4) 16.9 (6.5) NA (NA) 0.7
upper_out 805
97.3399999999999 13 (100%) 792 (100%) 0 (NA%)
upper_out2 805
71.24 13 (100%) 792 (100%) 0 (NA%)
lower_out 805
0.7 13 (100%) 792 (100%) 0 (NA%)
lower_out2 805
1.36 13 (100%) 792 (100%) 0 (NA%)
na_low 805
5.7 13 (100%) 792 (100%) 0 (NA%)
na_upp 805
90.8 13 (100%) 792 (100%) 0 (NA%)
duration_min_out2 805 <0.001
Above 95% quantile 13 (100%) 20 (2.5%) 0 (NA%)
Normal 0 (0%) 772 (97%) 0 (NA%)
Below 5% quantile 0 (0%) 0 (0%) 0 (NA%)
nas_out 805 0.2
Above 90% quantile 0 (0%) 0 (0%) 0 (NA%)
Normal 10 (77%) 691 (87%) 0 (NA%)
Below 10% quantile 3 (23%) 101 (13%) 0 (NA%)
odd_r 805
Normal 13 (100%) 792 (100%) 0 (NA%)
odd_r2 805
Normal 13 (100%) 792 (100%) 0 (NA%)
yrbr 805 1,985.5 (6.1) 1,982.5 (7.0) NA (NA) 0.2

1 n (%); Mean (SD)

2 Fisher's exact test; Wilcoxon rank sum test

Section III. Employment

sank %>%
  select(121:143, 175, 178:189) %>% 
  tbl_summary(by = duration_min_out, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section III. Employment (N = {N})**") %>% 
  bold_labels()
variable N Spend too much time, N = 131 Normal, N = 7921 Spend too little time, N = 01 p-value2
emp_status 805 0.7
1 9 (69%) 561 (71%) 0 (NA%)
2 0 (0%) 37 (4.7%) 0 (NA%)
3 1 (7.7%) 68 (8.6%) 0 (NA%)
4 2 (15%) 85 (11%) 0 (NA%)
5 0 (0%) 3 (0.4%) 0 (NA%)
6 0 (0%) 18 (2.3%) 0 (NA%)
7 1 (7.7%) 20 (2.5%) 0 (NA%)
emp_status_partner 805 0.014
1 4 (31%) 492 (62%) 0 (NA%)
2 4 (31%) 46 (5.8%) 0 (NA%)
3 1 (7.7%) 71 (9.0%) 0 (NA%)
4 2 (15%) 110 (14%) 0 (NA%)
5 0 (0%) 5 (0.6%) 0 (NA%)
6 1 (7.7%) 14 (1.8%) 0 (NA%)
7 1 (7.7%) 54 (6.8%) 0 (NA%)
work_home 805 0.4
-99 1 (7.7%) 22 (2.8%) 0 (NA%)
3 9 (69%) 535 (68%) 0 (NA%)
4 3 (23%) 235 (30%) 0 (NA%)
work_home_partner 805 0.004
-99 5 (38%) 64 (8.1%) 0 (NA%)
1 4 (31%) 463 (58%) 0 (NA%)
2 4 (31%) 265 (33%) 0 (NA%)
frontline 805 0.3
-99 1 (7.7%) 18 (2.3%) 0 (NA%)
1 4 (31%) 319 (40%) 0 (NA%)
2 8 (62%) 455 (57%) 0 (NA%)
frontline_partner 805 >0.9
-99 1 (7.7%) 63 (8.0%) 0 (NA%)
1 4 (31%) 289 (36%) 0 (NA%)
2 8 (62%) 440 (56%) 0 (NA%)
hrs_work_3 805 31.8 (40.0) 31.6 (30.0) NA (NA) 0.092
hrs_work_4 805 -74.1 (47.6) -32.9 (61.5) NA (NA) 0.018
hrs_work_partner_3 805 -14.2 (70.0) 19.1 (45.2) NA (NA) 0.3
hrs_work_partner_4 805 -57.7 (65.8) -39.3 (62.1) NA (NA) 0.3
min_commute_3 805 22.6 (37.7) 10.6 (43.9) NA (NA) 0.038
hrs_commute_3 805 0.4
-99 9 (69%) 340 (43%) 0 (NA%)
0 3 (23%) 159 (20%) 0 (NA%)
1 1 (7.7%) 225 (28%) 0 (NA%)
2 0 (0%) 54 (6.8%) 0 (NA%)
3 0 (0%) 12 (1.5%) 0 (NA%)
4 0 (0%) 2 (0.3%) 0 (NA%)
min_commute_partner_3 805 -29.8 (67.1) 0.2 (52.6) NA (NA) 0.2
hrs_commute_partnet_3 805 0.7
-99 8 (62%) 397 (50%) 0 (NA%)
0 3 (23%) 128 (16%) 0 (NA%)
1 2 (15%) 198 (25%) 0 (NA%)
2 0 (0%) 51 (6.4%) 0 (NA%)
3 0 (0%) 15 (1.9%) 0 (NA%)
4 0 (0%) 3 (0.4%) 0 (NA%)
rsn_nowork 232 0.6
-99 0 (0%) 20 (8.8%) 0 (NA%)
1 0 (0%) 8 (3.5%) 0 (NA%)
2 0 (0%) 48 (21%) 0 (NA%)
3 3 (75%) 129 (57%) 0 (NA%)
4 1 (25%) 23 (10%) 0 (NA%)
Unknown 9 564 0
rsn_nowork_partner 304 0.6
-99 1 (11%) 56 (19%) 0 (NA%)
1 0 (0%) 5 (1.7%) 0 (NA%)
2 1 (11%) 71 (24%) 0 (NA%)
3 7 (78%) 136 (46%) 0 (NA%)
4 0 (0%) 27 (9.2%) 0 (NA%)
Unknown 4 497 0
eip_1 805 0.4
-99 0 (0%) 38 (4.8%) 0 (NA%)
0 4 (31%) 371 (47%) 0 (NA%)
1 9 (69%) 383 (48%) 0 (NA%)
eip_2 805 0.4
-99 0 (0%) 38 (4.8%) 0 (NA%)
0 4 (31%) 366 (46%) 0 (NA%)
1 9 (69%) 388 (49%) 0 (NA%)
eip_3 805 0.3
-99 0 (0%) 38 (4.8%) 0 (NA%)
0 12 (92%) 550 (69%) 0 (NA%)
1 1 (7.7%) 204 (26%) 0 (NA%)
eip_4 805 0.4
-99 0 (0%) 38 (4.8%) 0 (NA%)
0 5 (38%) 425 (54%) 0 (NA%)
1 8 (62%) 329 (42%) 0 (NA%)
eip_5 805 >0.9
-99 0 (0%) 38 (4.8%) 0 (NA%)
0 8 (62%) 443 (56%) 0 (NA%)
1 5 (38%) 311 (39%) 0 (NA%)
eip_6 805 >0.9
-99 0 (0%) 38 (4.8%) 0 (NA%)
0 13 (100%) 743 (94%) 0 (NA%)
1 0 (0%) 11 (1.4%) 0 (NA%)
eip_7 805 >0.9
-99 0 (0%) 38 (4.8%) 0 (NA%)
0 13 (100%) 741 (94%) 0 (NA%)
1 0 (0%) 13 (1.6%) 0 (NA%)
number_missing_value 805 15.8 (4.4) 16.9 (6.5) NA (NA) 0.7
upper_out 805
97.3399999999999 13 (100%) 792 (100%) 0 (NA%)
upper_out2 805
71.24 13 (100%) 792 (100%) 0 (NA%)
lower_out 805
0.7 13 (100%) 792 (100%) 0 (NA%)
lower_out2 805
1.36 13 (100%) 792 (100%) 0 (NA%)
na_low 805
5.7 13 (100%) 792 (100%) 0 (NA%)
na_upp 805
90.8 13 (100%) 792 (100%) 0 (NA%)
duration_min_out2 805 <0.001
Above 95% quantile 13 (100%) 20 (2.5%) 0 (NA%)
Normal 0 (0%) 772 (97%) 0 (NA%)
Below 5% quantile 0 (0%) 0 (0%) 0 (NA%)
nas_out 805 0.2
Above 90% quantile 0 (0%) 0 (0%) 0 (NA%)
Normal 10 (77%) 691 (87%) 0 (NA%)
Below 10% quantile 3 (23%) 101 (13%) 0 (NA%)
odd_r 805
Normal 13 (100%) 792 (100%) 0 (NA%)
odd_r2 805
Normal 13 (100%) 792 (100%) 0 (NA%)
yrbr 805 1,985.5 (6.1) 1,982.5 (7.0) NA (NA) 0.2

1 n (%); Mean (SD)

2 Fisher's exact test; Wilcoxon rank sum test